The potential of monetary policy to stabilize fluctuations in output and employment is demonstrated in a stochastic rational expectations model in which firms choose, considering average profitability, to set prices in advance of the period when they apply to goods sold. This lead time in pricing decisions increases the fluctuations of output about the normal employment level. But proper use of a feedback monetary policy rule can reduce these fluctuations even though expectations are rational and people know the policy rule. It is noted that use of a rule-dictated policy sometimes requires the monetary authorities to penalize the economy in the short run for the sake of beneficial system effects of the rule upon the relevant steady-state distributions.

In this paper, we investigate the comparative properties of empirically estimated monetary models of the U.S. economy using a new database of models designed for such investigations. We focus on three representative models due to Christiano, Eichenbaum, and Evans (2005), Smets and Wouters (2007), and Taylor (1993a). Although these models differ in terms of structure, estimation method, sample period, and data vintage, we find surprisingly similar economic impacts of unanticipated changes in the federal funds rate. However, optimized monetary policy rules differ across models and lack robustness. Model averaging offers an effective strategy for improving the robustness of policy rules.

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper.The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates.When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models.It is, however, more flexible and general than these methods.The estimation method is based on the maximum likelihood principal.It is, as far as we know, the only method available for obtaining maximum likelihood estimates for nonlinear rational expectations models.The method has the advantage of being applicable to a wide range of models, including, as a special case, linear models.The method can also handle different assumptions about the expectations of the exogenous variables, something which is not true of currently available approaches to linear models.